Interrogating the Installation Gap and Potential of Solar Photovoltaic Systems Using GIS and Deep Learning

Abstract

Non-renewable-resource consumption and global greenhouse-gas (GHG) emissions are critical issues that pose a significant threat to sustainable development. Solar energy is a promising source to generate renewable energy and an appealing alternative electricity source for households. The primary goal of this research is to detect the rooftops that have no solar photovoltaic (PV) system deployed on them but that receive moderate to high solar-energy radiation using the Geographic Information System (GIS) and deep-learning techniques. Although various studies have been conducted on this subject, not many addressed these two issues simultaneously at a residential level. Identifying the installed solar PV systems in a large area can be expensive and time-consuming work if performed manually. Therefore, the deep-learning algorithm is an emerging alternative method to detect objects using aerial images. We employed the Single-Shot-Detector (SSD) model with the backbone of residual neural network 34 (ResNet34) to detect the solar PV systems and used GIS software to compute solar isolation and calculate the electricity production estimate (EPE) of each rooftop. Our results show that the SSD model detected 6010 solar panels on 4150 properties with an accuracy of 78% and observed that there were 176 Statistical Area 1s (SA1s) that had no rooftops with solar PV systems installed. Moreover, the total electricity production from the suitable area was estimated at over 929.8 Giga Watt-hours (GWhs) annually. Finally, the relation between solar-PV-system density and EPE was also identified using the bivariant correlation technique. Detecting the existing solar PV systems is useful in a broad range of applications including electricity-generation prediction, power-plant-production management, uncovering patterns between regions, etc. Examination of the spatial distribution of solar-energy potential in a region and performing an overlay analysis with socio-economic factors can help policymakers to understand the explanation behind the pattern and strategize the incentives accordingly.